This paper introduces techniques in Gaussian process regression model for spatiotemporal data collected from complex *** study focuses on extracting local structures and then constructing surrogate models based on Gau...
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This paper introduces techniques in Gaussian process regression model for spatiotemporal data collected from complex *** study focuses on extracting local structures and then constructing surrogate models based on Gaussian process *** proposed Dynamic Gaussian Process Regression(DGPR)consists of a sequence of local surrogate models related to each *** DGPR,the time-based spatial clustering is carried out to divide the systems into sub-spatio-temporal parts whose interior has similar variation patterns,where the temporal information is used as the prior information for training the spatial-surrogate *** DGPR is robust and especially suitable for the loosely coupled model structure,also allowing for parallel *** numerical results of the test function show the effectiveness of ***,the shock tube problem is successfully approximated under different phenomenon complexity.
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